v1.8.2

r-package/DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: geobr
 Title: Download Official Spatial Data Sets of Brazil
-Version: 1.8.999
+Version: 1.8.2
 Authors@R: c(person(given="Rafael H. M.", family="Pereira", email="[email protected]", role=c("aut", "cre"), comment = c(ORCID = "0000-0003-2125-7465")),
  person(given="Caio Nogueira", family="Goncalves", role=c("aut")),
  person(given="Paulo Henrique Fernandes de", family="Araujo", role=c("ctb")),

r-package/NEWS.md

@@ -1,7 +1,7 @@
-# geobr v1.8.999 dev 
+# geobr v1.8.2 
 
 **CRAN request**
-- Fixed issue to nake sure geobr uses suggested packages conditionally
+- Fixed issue to make sure geobr uses suggested packages conditionally
 
 **Minor changes**
 - Fixed non-ASCII characters in data `geobr::grid_state_correspondence_table()`

r-package/cran-comments.md

@@ -1,11 +1,13 @@
-── R CMD check results ─────────────────────────────────────────────────────────────────────────────── geobr 1.8.1 ────
-Duration: 7m 49.9s
+── R CMD check results ────────────────────────────────────────────────── geobr 1.8.2 ────
+Duration: 6m 47.4s
 
-❯ checking data for non-ASCII characters ... NOTE
- Note: found 58 marked UTF-8 strings
+0 errors ✔ | 0 warnings ✔ | 0 notes ✔
 
-0 errors ✔ | 0 warnings ✔ | 1 note ✖
 
 
-* geobr now uses suggested packages conditionally
+**CRAN request**
+- Fixed issue to make sure geobr uses suggested packages conditionally on CRAN
+
+**Minor changes**
+- Fixed non-ASCII characters in data `geobr::grid_state_correspondence_table()`
 

r-package/tests/tests_rafa/test_rafa.R

@@ -554,7 +554,9 @@ rhub::check_for_cran(show_status = FALSE)
 # submit to CRAN -----------------
 usethis::use_cran_comments('teste 2222, , asdadsad')
 
-1
+devtools::submit_cran()
+
+
 
 
 # build binary --------------------------------

r-package/vignettes/intro_to_geobr.Rmd

@@ -16,12 +16,6 @@ knitr::opts_chunk$set(
 )
 
 
-use_suggested_pkgs <- c((requireNamespace("dplyr")), 
- (requireNamespace("scales")), 
- (requireNamespace("ggplot2")), 
- (requireNamespace("censobr")))
-
-use_suggested_pkgs <- all(use_suggested_pkgs)
 ```
 
 
@@ -44,7 +38,7 @@ devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
 
 Now let's load the libraries we'll use in this vignette.
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, results='hide'}
+```{r message=FALSE, warning=FALSE, results='hide'}
 library(geobr)
 library(ggplot2)
 library(sf)
@@ -59,7 +53,7 @@ library(dplyr)
 
 The geobr package covers 27 spatial data sets, including a variety of political-administrative and statistical areas used in Brazil. You can view what data sets are available using the `list_geobr()` function.
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE}
+```{r message=FALSE, warning=FALSE}
 # Available data sets
 datasets <- list_geobr()
 
@@ -73,7 +67,7 @@ head(datasets)
 The syntax of all *geobr* functions operate one the same logic, so the code to download the data becomes intuitive for the user. Here are a few examples.
 
 Download an specific geographic area at a given year
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE}
+```{r message=FALSE, warning=FALSE}
 # State of Sergige
 state <- read_state(
  code_state="SE",
@@ -95,7 +89,7 @@ ggplot() +
 
 
 Download all geographic areas within a state at a given year
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, results='hide'}
+```{r message=FALSE, warning=FALSE, results='hide'}
 # All municipalities in the state of Minas Gerais
 muni <- read_municipality(code_muni = "MG", 
  year = 2007,
@@ -113,7 +107,7 @@ head(muni)
 
 If the parameter `code_` is not passed to the function, geobr returns the data for the whole country by default.
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE}
+```{r message=FALSE, warning=FALSE}
 # read all intermediate regions
 inter <- read_intermediate_region(
  year = 2017,
@@ -141,7 +135,7 @@ All functions to download polygon data such as states, municipalities etc. have
 
 Once you've downloaded the data, it is really simple to plot maps using `ggplot2`.
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"}
+```{r message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"}
 # Remove plot axis
 no_axis <- theme(axis.title=element_blank(),
  axis.text=element_blank(),
@@ -160,7 +154,7 @@ ggplot() +
 
 Plot all the municipalities of a particular state, such as Rio de Janeiro:
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"}
+```{r message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"}
 
 # Download all municipalities of Rio
 all_muni <- read_municipality(
@@ -187,7 +181,7 @@ The next step is to combine data from ***geobr*** package with other data sets
 
 First, we need a `data.frame` with estimates of Life Expectancy and merge it to our spatial database. The two-digit abbreviation of state name is our key column to join these two databases.
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, results='hide'}
+```{r message=FALSE, warning=FALSE, results='hide'}
 # Read data.frame with life expectancy data
 df <- utils::read.csv(system.file("extdata/br_states_lifexpect2017.csv", package = "geobr"), encoding = "UTF-8")
 
@@ -202,7 +196,7 @@ states <- dplyr::left_join(states, df, by = c("name_state" = "uf"))
 
 #### Plot thematic map
 
-```{r eval=use_suggested_pkgs, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center" }
+```{r message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center" }
 ggplot() +
  geom_sf(data=states, aes(fill=ESPVIDA2017), color= NA, size=.15) +
  labs(subtitle="Life Expectancy at birth, Brazilian States, 2014", size=8) +
@@ -220,7 +214,7 @@ First, we need to download households data from the Brazilian census using the `
 
 
 
-```{r, eval = use_suggested_pkgs }
+```{r}
 library(censobr)
 
 hs <- read_households(year = 2010, 
@@ -230,7 +224,7 @@ hs <- read_households(year = 2010,
 
 Now we're going to (a) group observations by municipality, (b) get the number of households connected to a sewage network, (c) calculate the proportion of households connected, and (d) collect the results.
 
-```{r, eval = use_suggested_pkgs, warning = FALSE}
+```{r, warning = FALSE}
 esg <- hs |> 
  collect() |>
  group_by(code_muni) |> # (a)
@@ -243,7 +237,7 @@ head(esg)
 ```
 Now we only need to download the geometries of Brazilian municipalities from **geobr**, merge the spatial data with our estimates and map the results.
 
-```{r, eval = use_suggested_pkgs, warning = FALSE}
+```{r, warning = FALSE}
 # download municipality geometries
 muni_sf <- geobr::read_municipality(year = 2010,
  showProgress = FALSE)